|Publication number||US8001118 B2|
|Application number||US 09/797,754|
|Publication date||Aug 16, 2011|
|Filing date||Mar 2, 2001|
|Priority date||Mar 2, 2001|
|Also published as||US8156100, US8352452, US20020123988, US20110179023, US20120226705|
|Publication number||09797754, 797754, US 8001118 B2, US 8001118B2, US-B2-8001118, US8001118 B2, US8001118B2|
|Inventors||Jeffrey A. Dean, Benedict Gomes, Krishna Bharat, Georges Harik, Monika H. Henzinger|
|Original Assignee||Google Inc.|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (19), Non-Patent Citations (1), Referenced by (17), Classifications (10), Legal Events (3)|
|External Links: USPTO, USPTO Assignment, Espacenet|
A. Field of the Invention
The present invention relates generally to information search and retrieval and, more particularly, to employing usage data to improve information search and retrieval.
B. Description of Related Art
The World Wide Web (“web”) contains a vast amount of information. Locating a desired portion of the information, however, can be challenging. This problem is compounded because the amount of information on the web and the number of new users inexperienced at web research are growing rapidly.
People generally surf the web based on its link graph structure, often starting with high quality human-maintained indices or search engines. Human-maintained lists cover popular topics effectively but are subjective, expensive to build and maintain, slow to improve, and do not cover all esoteric topics.
Automated search engines, in contrast, locate web sites by matching search terms entered by the user to an indexed corpus of web pages. Generally, the search engine returns a list of web sites sorted based on relevance to the user's search terms. Determining the correct relevance, or importance, of a web page to a user, however, can be a difficult task. For one thing, the importance of a web page to the user is inherently subjective and depends on the user's interests, knowledge, and attitudes. There is, however, much that can be determined objectively about the relative importance of a web page.
Conventional methods of determining relevance are based on matching a user's search terms to terms indexed from web pages. More advanced techniques determine the importance of a web page based on more than the content of the web page. For example, one known method, described in the article entitled “The Anatomy of a Large-Scale Hypertextual Search Engine,” by Sergey Brin and Lawrence Page, assigns a degree of importance to a web page based on the link structure of the web page.
Each of these conventional methods has shortcomings, however. Term-based methods are biased towards pages whose content or display is carefully chosen towards the given term-based method. Thus, they can be easily manipulated by the designers of the web page. Link-based methods have the problem that relatively new pages have usually fewer hyperlinks pointing to them than older pages, which tends to give a lower score to newer pages.
There exists, therefore, a need to develop other techniques for determining the importance of documents.
Systems and methods consistent with the present invention address this and other needs by identifying compounds based on the overall context of a user query. One aspect of the present invention is directed to a method of organizing a set of documents by receiving a search query and identifying a plurality of documents responsive to the search query. Each identified document is assigned a score based on usage information, and the documents are organized based on the assigned scores.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, explain the invention. In the drawings,
The following detailed description of the invention refers to the accompanying drawings. The detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims and equivalents.
In one embodiment, a search query is received and a list of responsive documents is identified. The list of responsive documents may be based on a comparison between the search query and the contents of the documents, or by other conventional methods. Usage statistics are determined for each document, and the documents are organized based in whole or in part on the usage statistics. These usage statistics may include, for example, the number of visitors to the document (perhaps over a period of time), the frequency with which the document was visited (perhaps over a period of time), or other measures.
The client devices 110 may include devices, such mainframes, minicomputers, personal computers, laptops, personal digital assistants, or the like, capable of connecting to the network 140. The client devices 110 may transmit data over the network 140 or receive data from the network 140 via a wired, wireless, or optical connection.
The bus 210 may include one or more conventional buses that permit communication among the components of the client device 110. The processor 220 may include any type of conventional processor or microprocessor that interprets and executes instructions. The main memory 230 may include a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by the processor 220. The ROM 240 may include a conventional ROM device or another type of static storage device that stores static information and instructions for use by the processor 220. The storage device 250 may include a magnetic and/or optical recording medium and its corresponding drive.
The input device 260 may include one or more conventional mechanisms that permit a user to input information to the client device 110, such as a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. The output device 270 may include one or more conventional mechanisms that output information to the user, including a display, a printer, a speaker, etc. The communication interface 280 may include any transceiver-like mechanism that enables the client device 110 to communicate with other devices and/or systems. For example, the communication interface 280 may include mechanisms for communicating with another device or system via a network, such as network 140.
As will be described in detail below, the client devices 110, consistent with the present invention, may perform certain document retrieval operations. The client devices 110 may perform these operations in response to processor 220 executing software instructions contained in a computer-readable medium, such as memory 230. A computer-readable medium may be defined as one or more memory devices and/or carrier waves. The software instructions may be read into memory 230 from another computer-readable medium, such as the data storage device 250, or from another device via the communication interface 280. The software instructions contained in memory 230 cause processor 220 to perform search-related activities described below. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes consistent with the present invention. Thus, the present invention is not limited to any specific combination of hardware circuitry and software.
The servers 120 and 130 may include one or more types of computer systems, such as a mainframe, minicomputer, or personal computer, capable of connecting to the network 140 to enable servers 120 and 130 to communicate with the client devices 110. In alternative implementations, the servers 120 and 130 may include mechanisms for directly connecting to one or more client devices 110. The servers 120 and 130 may transmit data over network 140 or receive data from the network 140 via a wired, wireless, or optical connection.
The servers may be configured in a manner similar to that described above in reference to
Once this set of responsive documents has been determined, it is necessary to organize the documents in some manner. Consistent with the invention, this may be achieved by employing usage statistics, in whole or in part.
As shown at stage 330, scores are assigned to each document based on the usage information. The scores may be absolute in value or relative to the scores for other documents. This process of assigning scores, which may occur before or after the set of responsive documents is identified, can be based on a variety of usage information. In a preferred implementation, the usage information comprises both unique visitor information and frequency of visit information, as described below in reference to
At stage 340, the responsive documents are organized based on the assigned scores. The documents may be organized based entirely on the scores derived from usage statistics. Alternatively, they may be organized based on the assigned scores in combination with other factors. For example, the documents may be organized based on the assigned scores combined with link information and/or query information. Link information involves the relationships between linked documents, and an example of the use of such link information is described in the Brin & Page publication referenced above. Query information involves the information provided as part of the search query, which may be used in a variety of ways to determine the relevance of a document. Other information, such as the length of the path of a document, could also be used.
In one implementation, documents are organized based on a total score that represents the product of a usage score and a standard query-term-based score (“IR score”). In particular, the total score equals the square root of the IR score multiplied by the usage score. The usage score, in turn, equals a frequency of visit score multiplied by a unique user score multiplied by a path length score.
The frequency of visit score equals log 2(1+log(VF)/log(MAXVF). VF is the number of times that the document was visited (or accessed) in one month, and MAXVF is set to 2000. A small value is used when VF is unknown. If the unique user is less than 10, it equals 0.5*UU/10; otherwise, it equals 0.5*(1+UU/MAXUU). UU is the number of unique hosts/IPs that access the document in one month, and MAXUU is set to 400. A small value is used when UU is unknown. The path length score equals log(K−PL)/log(K). PL is the number of ‘/’ characters in the document's path, and K is set to 20.
In other implementations, the raw count may be processed using any of a variety of techniques to develop a refined visit frequency, with a few such techniques being illustrated in
Instead of, or in addition to, filtering the raw count, the raw count may be weighted based on the nature of the visit (430). For example, one may wish to assign a weighting factor to a visit based on the geographic source for the visit (e.g., counting a visit from Germany as twice as important as a visit from Antarctica). Any other type of information that can be derived about the nature of the visit (e.g., the browser being used, information concerning the user, etc.) could also be used to weight the visit. This weighted visit frequency 430 may then be used as the refined visit frequency 440.
Although only a few techniques for computing the visit frequency are illustrated in
In other implementations, the raw count may be processed using any of a variety of techniques to develop a refined user count, with a few such techniques being illustrated in
Instead of, or in addition to, filtering the raw count, the raw count may be weighted based on the nature of the user (530). For example, one may wish to assign a weighting factor to a visit based on the geographic source for the visit (e.g., counting a user from Germany as twice as important as a user from Antarctica). Any other type of information that can be derived about the nature of the user (e.g., browsing history, bookmarked items, etc.) could also be used to weight the user. This weighted user information 530 may then be used as the refined user count 540.
Although only a few techniques for computing the number of users are illustrated in
Under a conventional term frequency based search method, the documents may be organized based on the frequency with which the search query term (“weather”) appears in the document. Accordingly, the documents may be organized into the following order: 620 (three occurrences of “weather”), 630 (two occurrences of “weather’), and 610 (one occurrence of “weather’).
Under a conventional link-based search method, the documents may be organized based on the number of other documents that link to those documents. Accordingly, the documents may be organized into the following order: 630 (linked to by two other documents), 620 (linked to by one other document), and 610 (linked to by no other documents).
Methods and apparatus consistent with the invention employ usage information to aid in organizing documents. Based purely on raw visit frequency, the documents may be organized into the following order: 610 (40 visits), 620 (30 visits), and 630 (4 visits). If these raw visit frequency number are refined to filter automated agents and to assign double weight to visits from Germany, the documents may be organized in the following order: 620 (effectively 40 visits, since the 10 from Germany count double), 610 (effectively 25 visits after filtering the 15 visits from automated agents), and 630 (effectively 4 visits).
Instead of using the usage information alone, the usage information may be used in combination with the query information and/or the link information to develop the ultimate organization of the documents.
The foregoing description of preferred embodiments of the present invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. For example, although the preceding description generally discussed the operation of search engine 125 in the context of a search of documents on the world wide web, search engine 125 could be implemented on any corpus.
In accordance with 37 C.F.R. §1.121(b)(1)(iii) separate sheets with the replacement paragraphs, marked up to show all changes relative to the previous version of the paragraphs, is filed herewith.
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|U.S. Classification||707/731, 707/727, 707/706, 707/728, 707/708, 707/716|
|International Classification||G06F17/00, G06F17/30|
|Mar 2, 2001||AS||Assignment|
Owner name: GOOGLE, INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:HENZINGER, MONIKA H.;HARIK, GEORGES R.;DEAN, JEFFREY A.;AND OTHERS;REEL/FRAME:011587/0107
Effective date: 20010301
|Nov 15, 2011||CC||Certificate of correction|
|Feb 16, 2015||FPAY||Fee payment|
Year of fee payment: 4